READ ME:

This document reproduces all tables and figures from "#polisci Twitter: A Descriptive Analysis of how Political Scientists Use Twitter in 2019".

The zipped file contains:
- pop_polisci_twitter_replication.R: The primary replication script. Running this from within the unzipped directory will generate all plots and tables from the paper in the ./Figures and ./Tables folders, respectively.
- pop_polisci_twitter_replication.RData: The main replication data, including:
	- adj.mat: a list of adjacency matrices where links are defined in different ways as outlined in the paper.
	- cols.list: a list of color vectors for various categories.
	- dyad.dat: the dyadic data indexed by follower (mentioner) / following (mentioning). 1,212,201 rows.
	- graph.list: a list of igraph objects corresponding to the links defined in the paper.
	- out: a list containing the raw Twitter data for each of the scholars in our collection.
	- phd.univ: dataframe of all academics at US-based PhD-granting institutions as of spring of 2019.
	- tweeters: dataframe of scholars at US-based PhD-granting institutions who use Twitter as of spring of 2019. 
	- a series of helper functions
- dyad_interactionspablo.RData: saved results from lines 447-501 (included to reduce computation time of replication).
- mk_ultrapablo.RData: saved results from lines 578-607 (included to reduce computation time of replication)
- quartile_interaction.RData: saved results from lines 703-745 (included to reduce computation time of replication). 

Running the pop_polisci_twitter_replication.R script as-is will reproduce all figures and tables in approximately five minutes of computation time, depending on your machine. Uncommenting lines 447-501, 578-607, and 703-745 will reproduce the dyad_interactionspablo.RData, mk_ultrapablo.RData, and quartile_interaction.RData files at the cost of much longer computation time (approximately 24 hours, depending on your machine). 